MiniMax plans to launch a large-scale model with 2.7 trillion parameters.
Related
MiniMax plans to launch a large model with 2.7 trillion parameters.
PANews reported on July 8th that, according to sources, AI company MiniMax is developing a new large-scale language model with 2.7 trillion parameters, exceeding the scale of all currently available domestic AI models. The sources stated that this new model is expected to be released as early as the third quarter of this year. Internal employees involved in the development have designated it as M3 Pro, but it is unclear whether the company will use this name in the official release. MiniMax plans to open-source the model. This new model is significantly larger than MiniMax's current flagship model, M3 (428 billion parameters). Larger-scale AI models are better suited for handling complex reasoning and multi-step instruction tasks.
Large-scale model stocks in Hong Kong extended their gains, with MINIMAX and Zhipu rising over 16%.
Mars Finance reported on July 8th that MINIMAX-W (00100.HK) rose 17%, and Zhipu (02513.HK) rose 16%. In terms of news, Zhipu, a leading Hong Kong-listed large-scale model manufacturer, saw its first share lock-up period expire today, with several core institutional investors clearly choosing to continue their investment. (Science and Technology Treasure Broadcast)
Some large-scale AI models in China are 90% cheaper than those in the US; Chinese AI's high cost-effectiveness is capturing the US market.
According to a report by CNBC on July 7th, influenced by the continued price increases of models from leading US AI vendors, Chinese AI large-scale models are rapidly expanding their application scale in US enterprises due to their cost-effectiveness advantage. Industry insiders point out that the performance of some leading open-source and open weighted models in China is currently about 6 to 9 months behind the technology of top-tier US models such as OpenAI and Anthropic, while the price is 60% to 90% lower, and they can cover the vast majority of routine AI tasks, thus gaining popularity among US enterprises. According to statistics from the AI model aggregation platform OpenRouter, since February 8th of this year, the proportion of Chinese AI models used by US enterprises has exceeded 30% weekly, reaching a peak of 46%; while the average proportion in the previous 12 months was 11%. Another industry statistic shows that in the first week of the launch of Zhipu's latest large-scale model GLM 5.2, the daily average number of word calls increased by 27 times and the number of customers increased by 80 times, making it the fastest-deployed model on the platform in 2026; US AI startup Lindy has significantly reduced costs after switching all its AI business to DeepSeek models, and expects to save millions of dollars within a few months. (CCTV Finance)
Ministry of Human Resources and Social Security: By 2030, the large-scale model of the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound.
According to Mars Finance, the Ministry of Human Resources and Social Security, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Data Administration recently jointly issued the "Implementation Opinions on Accelerating the Application and Development of 'Artificial Intelligence + Human Resources and Social Security'". A relevant official from the Ministry of Human Resources and Social Security answered reporters' questions regarding the "Implementation Opinions". In terms of promoting the work, a three-step work goal will be achieved within five years. First, building the foundation. Taking this year (2026) as a benchmark, the initial formation of the artificial intelligence application system, standard system, and guarantee system in the human resources and social security sector will be promoted. The infrastructure for "Artificial Intelligence + Human Resources and Social Security" applications will be deployed, a number of high-performance human resources and social security industry large-scale models and intelligent agent applications will be cultivated, and about 20 application scenarios based on human resources and social security industry large-scale models and corresponding high-quality datasets will be created, forming a collaborative development ecosystem of computing power, models, data, and scenario applications. Second, popularization and promotion. By 2027, a number of human resources and social security industry large-scale models and intelligent agents will be widely applied, and about 50 high-value application scenario empowerment paths will be explored, achieving significant results in intelligent development. Third, widespread application. By 2030, high-quality datasets will be effectively supplied, large-scale models for the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound, forming an innovative landscape where artificial intelligence is widely applied in the human resources and social security sector. (Cailian Press)
Opinion: Open source models account for only 10% of enterprise large-scale model spending, but mature production environments will be dominated by open source models.
According to Beating's monitoring, while public opinion often touts that open-source large models are dominating everything, enterprise spending data presents the opposite picture. Jesse Zhang, co-founder and CEO of Decagon, an enterprise-level AI customer service platform, points out that the share of open-source models in total enterprise spending has now dropped to 11%. This decline stems from the fact that most enterprises' AI applications are still in the early, undefined exploratory stage, thus defaulting to reliance on closed-source models. However, he emphasizes that once application scenarios mature, open-source models will take over production environments with their advantages of extremely low latency and deep fine-tuning. In Decagon's own production environment, 90% of calls have already switched to open-source weighted models. The core driver of this transformation is interaction speed and customization capabilities, not cost savings. In customer service scenarios, a single conversation that takes 8 seconds to finish will completely destroy the product experience. Since leading closed-source labs do not allow fine-tuning of flagship models, and small closed-source models cannot be deeply customized, small-sized open-source models, through fine-tuning for specific tasks, have become the only option to support high-frequency real-time interactions. The future of enterprise AI will see a division of labor: leading closed-source labs will continue to dominate the exploration and discovery of new fields, while open-source weighted models will increasingly take over the actual production of mature businesses. Because model fine-tuning requires extremely high levels of data and talent, the migration from closed-source to open-source will be a slow process lasting several years, during which both will experience sustained growth.
SpaceX AI and Cursor plan to launch their first jointly developed AI model as early as Wednesday.
PANews reported on July 8th that, according to Reuters citing The Information, SpaceX AI and Cursor plan to launch their first jointly developed AI model as early as Wednesday. The report stated that the model was originally scheduled for release earlier this week but was delayed due to efficiency optimizations. The new model is expected to have rapid information processing capabilities and, in some aspects, compete with Anthropic's Opus 4.8 and OpenAI's GPT 5.5. In June, SpaceX announced its acquisition of Anysphere, the company behind the AI programming assistant Cursor, in a $60 billion all-stock transaction to strengthen its position in the AI tools market. SpaceX (formerly xAI) was acquired by SpaceX in February of this year, and SpaceX officially joined the Nasdaq 100 index this Tuesday.